Expression of Interest: Machine Learning Engineer

What you'll do

• Design, train, and deploy large-scale ML models for programmatic advertising and commerce media products
• Architect and iterate on high-performance models to improve ad relevance, click-through rates, and conversion performance
• Productionize and maintain scalable ML pipelines from data ingestion to online inference
• Extract insights from massive datasets to define new features and refine modeling strategies
• Translate business goals into modeling challenges and success metrics in collaboration with Product and Data Science teams
• Validate innovations through rigorous experimentation including A/B tests and offline evaluations
• Balance marketplace health by harmonizing advertiser value with user experience
• Enhance system reliability by refining feature stores, monitoring data quality, and establishing debugging practices
• Drive platform evolution by contributing to ML tools and guidelines to increase experimentation velocity and deployment speed
• Optimize system performance by partnering with infrastructure teams to reduce latency and improve throughput
• Elevate technical excellence by sharing best practices and guiding technical direction of ML initiatives

Requirements

• 5-12+ years of experience delivering ML solutions in high-stakes production environments such as Ads, Recommenders, Search, or Marketplaces
• Strong foundation in ML and statistics applied to real-world business challenges
• Expertise in ranking, calibration, exploration-exploitation, causal inference, or multi-objective optimization
• Deep understanding of the end-to-end ML lifecycle including data pipelining, feature engineering, model architecture, inference optimization, and deployment
• Proficiency in Python, SQL, TensorFlow or PyTorch, and data processing stacks like Spark, Beam, or Flink
• Experience ensuring long-term health of ML systems in live environments, monitoring performance, diagnosing drift, and iterating based on feedback
• Technical mentorship skills and ability to share best practices for large-scale ML systems

Tech stack

Benefits